# Design Reliable CrewAI Agent Teams

Multi-agent projects often fail because roles, tasks, dependencies, and control flow are unclear. This skill provides practical CrewAI patterns for structured Python workflows.

## Install

```bash
npx skillstore add sickn33/crewai
```

## Metadata

- Status: approved
- Slug: sickn33-crewai
- Skillstore revision: r2
- Version status: missing
- Tree hash: 3ab6b3fe2a9b8f2a168b9c7db9d009620351e0ba953a53d7f00db78c5d6f5ac2
- Author: sickn33
- GitHub username: sickn33
- License: MIT
- Repository: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/crewai
- Ref: f9e2c34b4f19c7f3e6b0a1e93227b5f77cc12526
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: confirmation\_required
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands
- Quality score: 38
- Quality tier: warning
- Public page: https://skillstore.pages.dev/skills/sickn33-crewai
- Manifest: https://skillstore.pages.dev/api/skills/sickn33-crewai/manifest

## Capabilities

- Defines CrewAI agents with roles, goals, backstories, and assigned tools.
- Structures tasks with expected outputs, dependencies, and shared context.
- Recommends sequential or hierarchical orchestration based on workflow needs.
- Configures planning, memory, and event-driven CrewAI Flows.
- Shows class-based and decorator-based custom tool patterns.

## Use Cases

- Build a Research and Writing Crew: Design agents, tasks, and context dependencies for a repeatable research-to-content workflow.
- Choose an Orchestration Process: Compare sequential and hierarchical processes for a multi-agent workload with clear delegation needs.
- Plan a Stateful Agent Workflow: Map events, routers, memory, and specialized crews into a controlled CrewAI Flow.

## Prompt Templates

### Create a Basic Crew

```
Design a minimal CrewAI team for [goal]. Define each agent, task, expected output, and execution order. Explain the choices.
```

### Structure YAML Configuration

```
Convert this workflow into CrewAI agent and task configuration: [workflow]. Include roles, goals, dependencies, inputs, and expected outputs.
```

### Select the Process Model

```
Assess this multi-agent workflow: [details]. Recommend sequential or hierarchical execution. Identify delegation boundaries, dependencies, and failure points.
```

### Design a Production Flow

```
Design a CrewAI Flow for [system]. Include state, routing, memory, approval gates, tool permissions, error handling, observability, and test criteria.
```

## Limitations

- Provides guidance and examples but does not run or test CrewAI projects.
- Requires Python 3.10\+, CrewAI, an LLM provider, and project-specific credentials.
- Example code omits production controls such as authorization, input validation, and failure recovery.
- Features and APIs may differ across CrewAI versions.

## Best Practices

- Give every agent one clear responsibility and measurable expected outputs.
- Use least-privilege tools, validated inputs, and approval gates for sensitive actions.
- Test task dependencies, routing, memory behavior, retries, and partial failures.

## Anti Patterns

- Do not create many agents when a deterministic function or single model call is sufficient.
- Do not pass untrusted retrieved content into agent instructions without isolation and validation.
- Do not expose unrestricted database, filesystem, or network tools to autonomous agents.

## Security Audit

- Audited at: 2026-07-23T21:57:08.279\+00:00
- Summary: All seven static command findings are false positives caused by Markdown fences or inline code, with no Ruby or shell execution present. Semantic review identified unrestricted agent-controlled SQL execution and untrusted web research flowing into a downstream agent. Add database controls and indirect prompt injection defenses before publication.

## Stats

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- Downloads: 8
- Favorites: 0
- Popularity score: 0